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Implement vector search experimental feature (#1623)
* Implement vector search experimental feature * Update README.md --------- Co-authored-by: Clémentine U. - curqui <[email protected]>
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‎README.md‎

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@@ -963,7 +963,7 @@ client.index('myIndex').resetDictionary(): Promise<EnqueuedTask>
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### Proximity Precision <!-- omit in toc -->
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#### [Get proximity precision](https://www.meilisearch.com/docs/reference/api/settings#get-prximity-precision)
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#### [Get proximity precision](https://www.meilisearch.com/docs/reference/api/settings#get-proximity-precision)
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```ts
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client.index('myIndex').getProximityPrecision(): Promise<ProximityPrecision>
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client.index('myIndex').resetProximityPrecision(): Promise<EnqueuedTask>
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```
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### Embedders <!-- omit in toc -->
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⚠️ This feature is experimental. Activate the `vectorSearch` experimental feature to use it](https://www.meilisearch.com/docs/reference/api/experimental_features#configure-experimental-features)
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#### [Get embedders](https://www.meilisearch.com/docs/reference/api/settings#get-embedders)
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```ts
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client.index('myIndex').getEmbedders(): Promise<Embedders>
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```
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#### [Update embedders](https://www.meilisearch.com/docs/reference/api/settings#update-embedders)
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```ts
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client.index('myIndex').updateEmbedders(embedders: Embedders): Promise<EnqueuedTask>
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```
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#### [Reset embedders](https://www.meilisearch.com/docs/reference/api/settings#reset-embedders)
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```ts
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client.index('myIndex').resetEmbedders(): Promise<EnqueuedTask>
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```
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### Keys <!-- omit in toc -->
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#### [Get keys](https://www.meilisearch.com/docs/reference/api/keys#get-all-keys)

‎src/indexes.ts‎

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@@ -51,6 +51,7 @@ import {
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NonSeparatorTokens,
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Dictionary,
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ProximityPrecision,
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Embedders,
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} from './types'
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import { removeUndefinedFromObject } from './utils'
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import { HttpRequests } from './http-requests'
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return task
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}
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///
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/// EMBEDDERS
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///
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/**
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* Get the embedders settings of a Meilisearch index.
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*
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* @returns Promise containing the embedders settings
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*/
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async getEmbedders(): Promise<Embedders> {
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const url = `indexes/${this.uid}/settings/embedders`
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return await this.httpRequest.get<Embedders>(url)
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}
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/**
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* Update the embedders settings. Overwrite the old settings.
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*
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* @param embedders - Object that contains the new embedders settings.
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* @returns Promise containing an EnqueuedTask or null
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*/
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async updateEmbedders(embedders: Embedders): Promise<EnqueuedTask> {
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const url = `indexes/${this.uid}/settings/embedders`
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const task = await this.httpRequest.patch(url, embedders)
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return new EnqueuedTask(task)
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}
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/**
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* Reset the embedders settings to its default value
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*
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* @returns Promise containing an EnqueuedTask
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*/
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async resetEmbedders(): Promise<EnqueuedTask> {
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const url = `indexes/${this.uid}/settings/embedders`
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const task = await this.httpRequest.delete<EnqueuedTask>(url)
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task.enqueuedAt = new Date(task.enqueuedAt)
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return task
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}
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}
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export { Index }

‎src/types/types.ts‎

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processingTimeMs: number
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}
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export type HybridSearch = {
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embedder?: string
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semanticRatio?: number
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}
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export type SearchParams = Query &
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Pagination &
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Highlight &
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showRankingScore?: boolean
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showRankingScoreDetails?: boolean
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attributesToSearchOn?: string[] | null
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hybrid?: HybridSearch
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}
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// Search parameters for searches made with the GET method
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showMatchesPosition?: boolean
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vector?: string | null
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attributesToSearchOn?: string | null
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hybridEmbedder?: string
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hybridSemanticRatio?: number
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}
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export type MultiSearchQuery = SearchParams & { indexUid: string }
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export type Dictionary = string[] | null
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export type ProximityPrecision = 'byWord' | 'byAttribute'
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export type OpenAiEmbedder = {
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source: 'openAi'
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model?: string
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apiKey?: string
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documentTemplate?: string
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}
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export type HuggingFaceEmbedder = {
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source: 'huggingFace'
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model?: string
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revision?: string
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documentTemplate?: string
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}
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export type UserProvidedEmbedder = {
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source: 'userProvided'
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dimensions: number
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}
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export type Embedder =
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| OpenAiEmbedder
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| HuggingFaceEmbedder
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| UserProvidedEmbedder
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export type Embedders = Record<string, Embedder> | null
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export type FacetOrder = 'alpha' | 'count'
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export type Faceting = {
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nonSeparatorTokens?: NonSeparatorTokens
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dictionary?: Dictionary
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proximityPrecision?: ProximityPrecision
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embedders?: Embedders
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}
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/*

‎tests/__snapshots__/settings.test.ts.snap‎

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"*",
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],
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"distinctAttribute": null,
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"embedders": Object {},
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"faceting": Object {
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"maxValuesPerFacet": 100,
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"sortFacetValuesBy": Object {
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],
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"embedders": Object {},
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"sortFacetValuesBy": Object {
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"embedders": Object {},
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"sortFacetValuesBy": Object {
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"embedders": Object {},
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"sortFacetValuesBy": Object {
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"embedders": Object {},
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"embedders": Object {},
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"embedders": Object {},
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"faceting": Object {
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"embedders": Object {},
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"embedders": Object {},
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"embedders": Object {},
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"embedders": Object {},
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"embedders": Object {},
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